Pima Indians Diabetes Dataset

In this article, we use Kaggle'sPima Indians Diabetes. The Pima indians are a group of Native Americans living in an area consisting of what is now central and southern Arizona. A variety of statistical methods are used here for predictions.

Context

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Content

The datasets consist of several medical predictor variables and one target variable, Outcome. Predictor variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Dataset Analysis

Feature Explanations
Pregnancies Number of times pregnant
Glucose Plasma glucose concentration a 2 hours in an oral glucose tolerance test
Blood Pressure Diastolic blood pressure (mm Hg)
Skin Thickness Triceps skinfold thickness (mm)
Insulin 2-Hour serum insulin (mu U/ml)
BMI Body mass index (weight in kg/(height in m)^2)
Diabetes Pedigree Function Diabetes pedigree function
Age Age (years)
Outcome Whether or not a patient has diabetes

Modeling: PyTorch Artificial Neural Networks

We did go through PyTorch ANN in this article, and interested readers are encouraged to refer to that article.

Splitting the data into X and y sets:

Variance of the Features

We can standardize features by removing the mean and scaling to unit variance.

Train and Test Sets

The Model

Model Optimization Plot

Confusion Matrix


References

  1. Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C., & Johannes, R. S. (1988). Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proceedings of the Annual Symposium on Computer Application in Medical Care, 261–265.